KCentersAncestor

abstract val algorithmID: ClusteringAlgorithmNature

abstract val customCenters: HashMap[Int, V]

A custom centers initialiazation, empty HashMap by default which result usage of K++ initialization

abstract val epsilon: Double

The stopping criteria, ie the distance under which centers are mooving from their previous position

abstract val k: Int

The number of cluster seeked

abstract val maxIterations: Int

The maximum number of iterations

abstract val metric: D

The employed metric on the GVector descendant

abstract def run[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, V]]): CM

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def areCentersNotMovingEnough(updatedCenters: ArrayBuffer[(Int, V)], previousCenters: ArrayBuffer[(Int, V)], epsilon: Double, metric: D): Boolean

Check if centers move enough

final def asInstanceOf[T0]: T0

final def castModel(model: ClusteringModelLocal[V]): Option[CM]

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def obtainCenters[O, Cz[B, C <: GVector[C]] <: Clusterizable[B, C, Cz], GS[X] <: GenSeq[X]](data: GS[Cz[O, V]]): HashMap[Int, V]

Run the K-Centers

final def obtainNearestCenterID(v: V, centers: ArrayBuffer[(Int, V)], metric: D): ClusterID

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit